FN Archimer Export Format
PT J
TI Mapping the Intertidal Microphytobenthos Gross Primary Production Part I: Coupling Multispectral Remote Sensing and Physical Modeling
BT 
AF Méléder, Vona
   Savelli, Raphael
   Barnett, Alexandre
   Polsenaere, Pierre
   Gernez, Pierre
   Cugier, Philippe
   Lerouxel, Astrid
   Le Bris, Anthony
   Dupuy, Christine
   Le Fouest, Vincent
   Lavaud, Johann
AS 1:1;2:2;3:1,2;4:3;5:1;6:4;7:1;8:1,5;9:2;10:2;11:6,7;
FF 1:;2:;3:;4:PDG-ODE-LITTORAL-LERPC;5:;6:PDG-ODE-DYNECO-LEBCO;7:;8:;9:;10:;11:;
C1 Mer Molécules Santé (EA 21 60), Université de Nantes, Nantes, France
   LIENSs ‘Littoral ENvironnement et Sociétés’ UMR 7266, Institut du Littoral et de l’Environnement, CNRS/Université de La Rochelle, La Rochelle, France
   Laboratoire Environnement Ressources des Pertuis Charentais (LER-PC), Ifremer, L’Houmeau, France
   Département Dynamiques de l’Environnement Côtier, Laboratoire d’Ecologie Benthique, Ifremer, Plouzané, France
   Centre d’Etude et de Valorisation des Algues (CEVA), Pleubian, France
   Takuvik Joint International Laboratory UMI3376, CNRS (France) & ULaval (Canada), Département de Biologie, Université Laval, Québec, QC, Canada
   Takuvik Joint International Laboratory UMI3376, CNRS (France) & ULaval (Canada), Département de Biologie, Université Laval, Québec, QC, Canada
C2 UNIV NANTES, FRANCE
   UNIV LA ROCHELLE, FRANCE
   IFREMER, FRANCE
   IFREMER, FRANCE
   CEVA, FRANCE
   CNRS, FRANCE
   UNIV LAVAL, CANADA
SI LA TREMBLADE
   BREST
SE PDG-ODE-LITTORAL-LERPC
   PDG-ODE-DYNECO-LEBCO
IN WOS Ifremer UPR
   DOAJ
   copubli-france
   copubli-univ-france
   copubli-int-hors-europe
IF 5.247
TC 17
UR https://archimer.ifremer.fr/doc/00643/75531/76429.pdf
   https://archimer.ifremer.fr/doc/00643/75531/76430.docx
LA English
DT Article
DE ;microphytobenthos;intertidal mudflat;gross primary production;remote sensing;NDVI;modeling
AB The gross primary production (GPP) of intertidal mudflat microphytobenthos supports important ecosystem services such as shoreline stabilization and food production, and it contributes to blue carbon. However, monitoring microphytobenthos GPP over a long-term and large spatial scale is rendered difficult by its high temporal and spatial variability. To overcome this issue, we developed an algorithm to map microphytobenthos GPP in which the following are coupled: (i) NDVI maps derived from high spatial resolution satellite images (SPOT6 or Pléiades), estimating the horizontal distribution of the microphytobenthos biomass; (ii) emersion time, photosynthetically active radiation (PAR), and mud surface temperature simulated from the physical model MARS-3D; (iii) photophysiological parameters retrieved from Production–irradiance (P–E) curves, obtained under controlled conditions of PAR and temperature, using benthic chambers, and expressing the production rate into mg C h–1 m–2 ndvi–1. The productivity was directly calibrated to NDVI to be consistent with remote-sensing measurements of microphytobenthos biomass and was spatially upscaled using satellite-derived NDVI maps acquired at different seasons. The remotely sensed microphytobenthos GPP reasonably compared with in situ GPP measurements. It was highest in March with a daily production reaching 50.2 mg C m–2 d–1, and lowest in July with a daily production of 22.3 mg C m–2 d–1. Our remote sensing algorithm is a new step in the perspective of mapping microphytobenthos GPP over large mudflats to estimate its actual contribution to ecosystem functions, including blue carbon, from local and global scales. 
PY 2020
PD JUN
SO Frontiers In Marine Science
SN 2296-7745
PU Frontiers Media SA
VL 7
UT 000552273300001
DI 10.3389/fmars.2020.00520
ID 75531
ER

EF